ArXiv Paperboy (Stat.ME+Econ.EM)
@paperposterbot.bsky.social
posts updates from arXiv rss feeds for methodology papers in Statistics and Econometrics. Also maintains an arxiv and posts random papers from it.
maintainer: @apoorvalal.com
source code: https://github.com/apoorvalal/bsky_paperbot
maintainer: @apoorvalal.com
source code: https://github.com/apoorvalal/bsky_paperbot
link 📈🤖
A Linear Relationship between Correlation and Cohen's Kappa for Binary Data and Simulating Multivariate Nominal and Ordinal Data with Specified Kappa Matrix () arXiv:2404.14149v1 Announce Type: new
Abstract: Cohen's kappa is a useful measure for agreement between the judges, inter-rater
A Linear Relationship between Correlation and Cohen's Kappa for Binary Data and Simulating Multivariate Nominal and Ordinal Data with Specified Kappa Matrix () arXiv:2404.14149v1 Announce Type: new
Abstract: Cohen's kappa is a useful measure for agreement between the judges, inter-rater
November 26, 2025 at 3:27 AM
link 📈🤖
A Linear Relationship between Correlation and Cohen's Kappa for Binary Data and Simulating Multivariate Nominal and Ordinal Data with Specified Kappa Matrix () arXiv:2404.14149v1 Announce Type: new
Abstract: Cohen's kappa is a useful measure for agreement between the judges, inter-rater
A Linear Relationship between Correlation and Cohen's Kappa for Binary Data and Simulating Multivariate Nominal and Ordinal Data with Specified Kappa Matrix () arXiv:2404.14149v1 Announce Type: new
Abstract: Cohen's kappa is a useful measure for agreement between the judges, inter-rater
link 📈🤖
Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal () In many settings, interventions may be more effective for some individuals
than others, so that targeting interventions may be beneficial. We analyze the
value of target
Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal () In many settings, interventions may be more effective for some individuals
than others, so that targeting interventions may be beneficial. We analyze the
value of target
November 26, 2025 at 1:34 AM
link 📈🤖
Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal () In many settings, interventions may be more effective for some individuals
than others, so that targeting interventions may be beneficial. We analyze the
value of target
Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal () In many settings, interventions may be more effective for some individuals
than others, so that targeting interventions may be beneficial. We analyze the
value of target
link 📈🤖
Far beyond day-ahead with econometric models for electricity price forecasting () arXiv:2406.00326v1 Announce Type: cross
Abstract: The surge in global energy prices during the recent energy crisis, which peaked in 2022, has intensified the need for mid-term to long-term forecasting for
Far beyond day-ahead with econometric models for electricity price forecasting () arXiv:2406.00326v1 Announce Type: cross
Abstract: The surge in global energy prices during the recent energy crisis, which peaked in 2022, has intensified the need for mid-term to long-term forecasting for
November 25, 2025 at 10:06 PM
link 📈🤖
Far beyond day-ahead with econometric models for electricity price forecasting () arXiv:2406.00326v1 Announce Type: cross
Abstract: The surge in global energy prices during the recent energy crisis, which peaked in 2022, has intensified the need for mid-term to long-term forecasting for
Far beyond day-ahead with econometric models for electricity price forecasting () arXiv:2406.00326v1 Announce Type: cross
Abstract: The surge in global energy prices during the recent energy crisis, which peaked in 2022, has intensified the need for mid-term to long-term forecasting for
link 📈🤖
Differences in academic preparedness do not fully explain Black-White enrollment disparities in advanced high school coursework () arXiv:2306.15075v2 Announce Type: replace
Abstract: Whether racial disparities in enrollment in advanced high school coursework can be attributed to differen
Differences in academic preparedness do not fully explain Black-White enrollment disparities in advanced high school coursework () arXiv:2306.15075v2 Announce Type: replace
Abstract: Whether racial disparities in enrollment in advanced high school coursework can be attributed to differen
November 25, 2025 at 7:05 PM
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Differences in academic preparedness do not fully explain Black-White enrollment disparities in advanced high school coursework () arXiv:2306.15075v2 Announce Type: replace
Abstract: Whether racial disparities in enrollment in advanced high school coursework can be attributed to differen
Differences in academic preparedness do not fully explain Black-White enrollment disparities in advanced high school coursework () arXiv:2306.15075v2 Announce Type: replace
Abstract: Whether racial disparities in enrollment in advanced high school coursework can be attributed to differen
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Evaluating the Impact of Regulatory Policies on Social Welfare in Difference-in-Difference Settings (Ghanem, K\'edagni, Mourifi\'e) Quantifying the impact of regulatory policies on social welfare generally requires the identification of counterfactual distributions. Many of these policies
Evaluating the Impact of Regulatory Policies on Social Welfare in Difference-in-Difference Settings (Ghanem, K\'edagni, Mourifi\'e) Quantifying the impact of regulatory policies on social welfare generally requires the identification of counterfactual distributions. Many of these policies
November 25, 2025 at 6:05 PM
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Evaluating the Impact of Regulatory Policies on Social Welfare in Difference-in-Difference Settings (Ghanem, K\'edagni, Mourifi\'e) Quantifying the impact of regulatory policies on social welfare generally requires the identification of counterfactual distributions. Many of these policies
Evaluating the Impact of Regulatory Policies on Social Welfare in Difference-in-Difference Settings (Ghanem, K\'edagni, Mourifi\'e) Quantifying the impact of regulatory policies on social welfare generally requires the identification of counterfactual distributions. Many of these policies
link 📈🤖
Experimental Design under Network Interference (Viviano) This paper studies how to design two-wave experiments in the presence of spillovers for precise inference on treatment effects. We consider units connected through a single network, local dependence among individuals, and a general
Experimental Design under Network Interference (Viviano) This paper studies how to design two-wave experiments in the presence of spillovers for precise inference on treatment effects. We consider units connected through a single network, local dependence among individuals, and a general
November 25, 2025 at 6:01 PM
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Experimental Design under Network Interference (Viviano) This paper studies how to design two-wave experiments in the presence of spillovers for precise inference on treatment effects. We consider units connected through a single network, local dependence among individuals, and a general
Experimental Design under Network Interference (Viviano) This paper studies how to design two-wave experiments in the presence of spillovers for precise inference on treatment effects. We consider units connected through a single network, local dependence among individuals, and a general
link 📈🤖
The Unified Non-Convex Framework for Robust Causal Inference: Overcoming the Gaussian Barrier and Optimization Fragility (Uehara) This document proposes a Unified Robust Framework that re-engineers the estimation of the Average Treatment Effect on the Overlap (ATO). It synthesizes gamma-D
The Unified Non-Convex Framework for Robust Causal Inference: Overcoming the Gaussian Barrier and Optimization Fragility (Uehara) This document proposes a Unified Robust Framework that re-engineers the estimation of the Average Treatment Effect on the Overlap (ATO). It synthesizes gamma-D
November 25, 2025 at 5:59 PM
link 📈🤖
The Unified Non-Convex Framework for Robust Causal Inference: Overcoming the Gaussian Barrier and Optimization Fragility (Uehara) This document proposes a Unified Robust Framework that re-engineers the estimation of the Average Treatment Effect on the Overlap (ATO). It synthesizes gamma-D
The Unified Non-Convex Framework for Robust Causal Inference: Overcoming the Gaussian Barrier and Optimization Fragility (Uehara) This document proposes a Unified Robust Framework that re-engineers the estimation of the Average Treatment Effect on the Overlap (ATO). It synthesizes gamma-D
link 📈🤖
Energy-Efficient Routing Protocol in Vehicular Opportunistic Networks: A Dynamic Cluster-based Routing Using Deep Reinforcement Learning (Sani, Iranmanesh, Raad et al) Opportunistic Networks (OppNets) employ the Store-Carry-Forward (SCF) paradigm to maintain communication during intermitt
Energy-Efficient Routing Protocol in Vehicular Opportunistic Networks: A Dynamic Cluster-based Routing Using Deep Reinforcement Learning (Sani, Iranmanesh, Raad et al) Opportunistic Networks (OppNets) employ the Store-Carry-Forward (SCF) paradigm to maintain communication during intermitt
November 25, 2025 at 5:58 PM
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Energy-Efficient Routing Protocol in Vehicular Opportunistic Networks: A Dynamic Cluster-based Routing Using Deep Reinforcement Learning (Sani, Iranmanesh, Raad et al) Opportunistic Networks (OppNets) employ the Store-Carry-Forward (SCF) paradigm to maintain communication during intermitt
Energy-Efficient Routing Protocol in Vehicular Opportunistic Networks: A Dynamic Cluster-based Routing Using Deep Reinforcement Learning (Sani, Iranmanesh, Raad et al) Opportunistic Networks (OppNets) employ the Store-Carry-Forward (SCF) paradigm to maintain communication during intermitt
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Uncertainty of Network Topology with Applications to Out-of-Distribution Detection (Yeh, Yang) Persistent homology (PH) is a crucial concept in computational topology, providing a multiscale topological description of a space. It is particularly significant in topological data analysis, w
Uncertainty of Network Topology with Applications to Out-of-Distribution Detection (Yeh, Yang) Persistent homology (PH) is a crucial concept in computational topology, providing a multiscale topological description of a space. It is particularly significant in topological data analysis, w
November 25, 2025 at 5:56 PM
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Uncertainty of Network Topology with Applications to Out-of-Distribution Detection (Yeh, Yang) Persistent homology (PH) is a crucial concept in computational topology, providing a multiscale topological description of a space. It is particularly significant in topological data analysis, w
Uncertainty of Network Topology with Applications to Out-of-Distribution Detection (Yeh, Yang) Persistent homology (PH) is a crucial concept in computational topology, providing a multiscale topological description of a space. It is particularly significant in topological data analysis, w
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Prior-Free Information Design (Rosenthal) This paper introduces a prior-free framework for information design based on partial identification and applies it to robust causal inference. The decision maker observes the distribution of signals generated by an information structure and ranks
Prior-Free Information Design (Rosenthal) This paper introduces a prior-free framework for information design based on partial identification and applies it to robust causal inference. The decision maker observes the distribution of signals generated by an information structure and ranks
November 25, 2025 at 5:54 PM
link 📈🤖
Prior-Free Information Design (Rosenthal) This paper introduces a prior-free framework for information design based on partial identification and applies it to robust causal inference. The decision maker observes the distribution of signals generated by an information structure and ranks
Prior-Free Information Design (Rosenthal) This paper introduces a prior-free framework for information design based on partial identification and applies it to robust causal inference. The decision maker observes the distribution of signals generated by an information structure and ranks
link 📈🤖
Estimation of High-dimensional Nonlinear Vector Autoregressive Models (Han, Chen, Wu) High-dimensional vector autoregressive (VAR) models have numerous applications in fields such as econometrics, biology, climatology, among others. While prior research has mainly focused on linear VAR mo
Estimation of High-dimensional Nonlinear Vector Autoregressive Models (Han, Chen, Wu) High-dimensional vector autoregressive (VAR) models have numerous applications in fields such as econometrics, biology, climatology, among others. While prior research has mainly focused on linear VAR mo
November 25, 2025 at 5:51 PM
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Estimation of High-dimensional Nonlinear Vector Autoregressive Models (Han, Chen, Wu) High-dimensional vector autoregressive (VAR) models have numerous applications in fields such as econometrics, biology, climatology, among others. While prior research has mainly focused on linear VAR mo
Estimation of High-dimensional Nonlinear Vector Autoregressive Models (Han, Chen, Wu) High-dimensional vector autoregressive (VAR) models have numerous applications in fields such as econometrics, biology, climatology, among others. While prior research has mainly focused on linear VAR mo
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Majority of the Bests: Improving Best-of-N via Bootstrapping (Rakhsha, Madan, Zhang et al) Sampling multiple outputs from a Large Language Model (LLM) and selecting the most frequent (Self-consistency) or highest-scoring (Best-of-N) candidate is a popular approach to achieve higher accura
Majority of the Bests: Improving Best-of-N via Bootstrapping (Rakhsha, Madan, Zhang et al) Sampling multiple outputs from a Large Language Model (LLM) and selecting the most frequent (Self-consistency) or highest-scoring (Best-of-N) candidate is a popular approach to achieve higher accura
November 25, 2025 at 5:48 PM
link 📈🤖
Majority of the Bests: Improving Best-of-N via Bootstrapping (Rakhsha, Madan, Zhang et al) Sampling multiple outputs from a Large Language Model (LLM) and selecting the most frequent (Self-consistency) or highest-scoring (Best-of-N) candidate is a popular approach to achieve higher accura
Majority of the Bests: Improving Best-of-N via Bootstrapping (Rakhsha, Madan, Zhang et al) Sampling multiple outputs from a Large Language Model (LLM) and selecting the most frequent (Self-consistency) or highest-scoring (Best-of-N) candidate is a popular approach to achieve higher accura
link 📈🤖
An operator splitting analysis of Wasserstein--Fisher--Rao gradient flows (Crucinio, Pathiraja) Wasserstein-Fisher-Rao (WFR) gradient flows have been recently proposed as a powerful sampling tool that combines the advantages of pure Wasserstein (W) and pure Fisher-Rao (FR) gradient flows.
An operator splitting analysis of Wasserstein--Fisher--Rao gradient flows (Crucinio, Pathiraja) Wasserstein-Fisher-Rao (WFR) gradient flows have been recently proposed as a powerful sampling tool that combines the advantages of pure Wasserstein (W) and pure Fisher-Rao (FR) gradient flows.
November 25, 2025 at 5:45 PM
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An operator splitting analysis of Wasserstein--Fisher--Rao gradient flows (Crucinio, Pathiraja) Wasserstein-Fisher-Rao (WFR) gradient flows have been recently proposed as a powerful sampling tool that combines the advantages of pure Wasserstein (W) and pure Fisher-Rao (FR) gradient flows.
An operator splitting analysis of Wasserstein--Fisher--Rao gradient flows (Crucinio, Pathiraja) Wasserstein-Fisher-Rao (WFR) gradient flows have been recently proposed as a powerful sampling tool that combines the advantages of pure Wasserstein (W) and pure Fisher-Rao (FR) gradient flows.
link 📈🤖
Hierarchical Linkage Clustering Beyond Binary Trees and Ultrametrics (Dreveton, Grossglauser, Kuroda et al) Hierarchical clustering seeks to uncover nested structures in data by constructing a tree of clusters, where deeper levels reveal finer-grained relationships. Traditional methods, i
Hierarchical Linkage Clustering Beyond Binary Trees and Ultrametrics (Dreveton, Grossglauser, Kuroda et al) Hierarchical clustering seeks to uncover nested structures in data by constructing a tree of clusters, where deeper levels reveal finer-grained relationships. Traditional methods, i
November 25, 2025 at 5:43 PM
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Hierarchical Linkage Clustering Beyond Binary Trees and Ultrametrics (Dreveton, Grossglauser, Kuroda et al) Hierarchical clustering seeks to uncover nested structures in data by constructing a tree of clusters, where deeper levels reveal finer-grained relationships. Traditional methods, i
Hierarchical Linkage Clustering Beyond Binary Trees and Ultrametrics (Dreveton, Grossglauser, Kuroda et al) Hierarchical clustering seeks to uncover nested structures in data by constructing a tree of clusters, where deeper levels reveal finer-grained relationships. Traditional methods, i
link 📈🤖
Divergence-Minimization for Latent-Structure Models: Monotone Operators, Contraction Guarantees, and Robust Inference (Li, Vidyashankar) We develop a divergence-minimization (DM) framework for robust and efficient inference in latent-mixture models. By optimizing a residual-adjusted diver
Divergence-Minimization for Latent-Structure Models: Monotone Operators, Contraction Guarantees, and Robust Inference (Li, Vidyashankar) We develop a divergence-minimization (DM) framework for robust and efficient inference in latent-mixture models. By optimizing a residual-adjusted diver
November 25, 2025 at 5:40 PM
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Divergence-Minimization for Latent-Structure Models: Monotone Operators, Contraction Guarantees, and Robust Inference (Li, Vidyashankar) We develop a divergence-minimization (DM) framework for robust and efficient inference in latent-mixture models. By optimizing a residual-adjusted diver
Divergence-Minimization for Latent-Structure Models: Monotone Operators, Contraction Guarantees, and Robust Inference (Li, Vidyashankar) We develop a divergence-minimization (DM) framework for robust and efficient inference in latent-mixture models. By optimizing a residual-adjusted diver
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A novel k-means clustering approach using two distance measures for Gaussian data (Technology)) Clustering algorithms have long been the topic of research, representing the more popular side of unsupervised learning. Since clustering analysis is one of the best ways to find some clarity a
A novel k-means clustering approach using two distance measures for Gaussian data (Technology)) Clustering algorithms have long been the topic of research, representing the more popular side of unsupervised learning. Since clustering analysis is one of the best ways to find some clarity a
November 25, 2025 at 5:39 PM
link 📈🤖
A novel k-means clustering approach using two distance measures for Gaussian data (Technology)) Clustering algorithms have long been the topic of research, representing the more popular side of unsupervised learning. Since clustering analysis is one of the best ways to find some clarity a
A novel k-means clustering approach using two distance measures for Gaussian data (Technology)) Clustering algorithms have long been the topic of research, representing the more popular side of unsupervised learning. Since clustering analysis is one of the best ways to find some clarity a
link 📈🤖
Copula Based Fusion of Clinical and Genomic Machine Learning Risk Scores for Breast Cancer Risk Stratification (Aich, Hewage, Murshed) Clinical and genomic models are both used to predict breast cancer outcomes, but they are often combined using simple linear rules that do not account for
Copula Based Fusion of Clinical and Genomic Machine Learning Risk Scores for Breast Cancer Risk Stratification (Aich, Hewage, Murshed) Clinical and genomic models are both used to predict breast cancer outcomes, but they are often combined using simple linear rules that do not account for
November 25, 2025 at 5:37 PM
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Copula Based Fusion of Clinical and Genomic Machine Learning Risk Scores for Breast Cancer Risk Stratification (Aich, Hewage, Murshed) Clinical and genomic models are both used to predict breast cancer outcomes, but they are often combined using simple linear rules that do not account for
Copula Based Fusion of Clinical and Genomic Machine Learning Risk Scores for Breast Cancer Risk Stratification (Aich, Hewage, Murshed) Clinical and genomic models are both used to predict breast cancer outcomes, but they are often combined using simple linear rules that do not account for
link 📈🤖
Random Text, Zipf's Law, Critical Length,and Implications for Large Language Models (Berman) We study a deliberately simple, fully non-linguistic model of text: a sequence of independent draws from a finite alphabet of letters plus a single space symbol. A word is defined as a maximal blo
Random Text, Zipf's Law, Critical Length,and Implications for Large Language Models (Berman) We study a deliberately simple, fully non-linguistic model of text: a sequence of independent draws from a finite alphabet of letters plus a single space symbol. A word is defined as a maximal blo
November 25, 2025 at 5:36 PM
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Random Text, Zipf's Law, Critical Length,and Implications for Large Language Models (Berman) We study a deliberately simple, fully non-linguistic model of text: a sequence of independent draws from a finite alphabet of letters plus a single space symbol. A word is defined as a maximal blo
Random Text, Zipf's Law, Critical Length,and Implications for Large Language Models (Berman) We study a deliberately simple, fully non-linguistic model of text: a sequence of independent draws from a finite alphabet of letters plus a single space symbol. A word is defined as a maximal blo
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Hierarchical Bayesian spectral analysis of multiple stationary time series (Lee, Coulter, Siegle et al) The power spectrum of biomedical time series provides important indirect measurements of physiological processes underlying health and biological functions. However, simultaneously char
Hierarchical Bayesian spectral analysis of multiple stationary time series (Lee, Coulter, Siegle et al) The power spectrum of biomedical time series provides important indirect measurements of physiological processes underlying health and biological functions. However, simultaneously char
November 25, 2025 at 5:34 PM
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Hierarchical Bayesian spectral analysis of multiple stationary time series (Lee, Coulter, Siegle et al) The power spectrum of biomedical time series provides important indirect measurements of physiological processes underlying health and biological functions. However, simultaneously char
Hierarchical Bayesian spectral analysis of multiple stationary time series (Lee, Coulter, Siegle et al) The power spectrum of biomedical time series provides important indirect measurements of physiological processes underlying health and biological functions. However, simultaneously char
link 📈🤖
Asymptotic linear dependence and ellipse statistics for multivariate two-sample homogeneity test (Shen, Fu, Chen et al) Statistical depth, which measures the center-outward rank of a given sample with respect to its underlying distribution, has become a popular and powerful tool in nonpar
Asymptotic linear dependence and ellipse statistics for multivariate two-sample homogeneity test (Shen, Fu, Chen et al) Statistical depth, which measures the center-outward rank of a given sample with respect to its underlying distribution, has become a popular and powerful tool in nonpar
November 25, 2025 at 5:29 PM
link 📈🤖
Asymptotic linear dependence and ellipse statistics for multivariate two-sample homogeneity test (Shen, Fu, Chen et al) Statistical depth, which measures the center-outward rank of a given sample with respect to its underlying distribution, has become a popular and powerful tool in nonpar
Asymptotic linear dependence and ellipse statistics for multivariate two-sample homogeneity test (Shen, Fu, Chen et al) Statistical depth, which measures the center-outward rank of a given sample with respect to its underlying distribution, has become a popular and powerful tool in nonpar
link 📈🤖
Product Depth for Temporal Point Processes Observed Only Up to the First k Events (Shen, Fu, Shi et al) Temporal point processes (TPPs) model the timing of discrete events along a timeline and are widely used in fields such as neuroscience and fi- nance. Statistical depth functions are po
Product Depth for Temporal Point Processes Observed Only Up to the First k Events (Shen, Fu, Shi et al) Temporal point processes (TPPs) model the timing of discrete events along a timeline and are widely used in fields such as neuroscience and fi- nance. Statistical depth functions are po
November 25, 2025 at 5:28 PM
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Product Depth for Temporal Point Processes Observed Only Up to the First k Events (Shen, Fu, Shi et al) Temporal point processes (TPPs) model the timing of discrete events along a timeline and are widely used in fields such as neuroscience and fi- nance. Statistical depth functions are po
Product Depth for Temporal Point Processes Observed Only Up to the First k Events (Shen, Fu, Shi et al) Temporal point processes (TPPs) model the timing of discrete events along a timeline and are widely used in fields such as neuroscience and fi- nance. Statistical depth functions are po
link 📈🤖
Identification, estimation and inference in Panel Vector Autoregressions using external instruments (Pala) This paper proposes an identification inspired from the SVAR-IV literature that uses external instruments to identify PVARs, and discusses associated issues of identification, estima
Identification, estimation and inference in Panel Vector Autoregressions using external instruments (Pala) This paper proposes an identification inspired from the SVAR-IV literature that uses external instruments to identify PVARs, and discusses associated issues of identification, estima
November 25, 2025 at 5:25 PM
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Identification, estimation and inference in Panel Vector Autoregressions using external instruments (Pala) This paper proposes an identification inspired from the SVAR-IV literature that uses external instruments to identify PVARs, and discusses associated issues of identification, estima
Identification, estimation and inference in Panel Vector Autoregressions using external instruments (Pala) This paper proposes an identification inspired from the SVAR-IV literature that uses external instruments to identify PVARs, and discusses associated issues of identification, estima
link 📈🤖
Community-level core-periphery structures in collaboration networks (Geremia, Stefano, Fop) Uncovering structural patterns in collaboration networks is key for understanding how knowledge flows and innovation emerges. These networks often exhibit a rich interplay of meso-scale structures,
Community-level core-periphery structures in collaboration networks (Geremia, Stefano, Fop) Uncovering structural patterns in collaboration networks is key for understanding how knowledge flows and innovation emerges. These networks often exhibit a rich interplay of meso-scale structures,
November 25, 2025 at 5:22 PM
link 📈🤖
Community-level core-periphery structures in collaboration networks (Geremia, Stefano, Fop) Uncovering structural patterns in collaboration networks is key for understanding how knowledge flows and innovation emerges. These networks often exhibit a rich interplay of meso-scale structures,
Community-level core-periphery structures in collaboration networks (Geremia, Stefano, Fop) Uncovering structural patterns in collaboration networks is key for understanding how knowledge flows and innovation emerges. These networks often exhibit a rich interplay of meso-scale structures,
link 📈🤖
Integrating Complex Covariate Transformations in Generalized Additive Models (Collarin, Fasiolo, Goude et al) Transformations of covariates are widely used in applied statistics to improve interpretability and to satisfy assumptions required for valid inference. More broadly, feature engi
Integrating Complex Covariate Transformations in Generalized Additive Models (Collarin, Fasiolo, Goude et al) Transformations of covariates are widely used in applied statistics to improve interpretability and to satisfy assumptions required for valid inference. More broadly, feature engi
November 25, 2025 at 5:17 PM
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Integrating Complex Covariate Transformations in Generalized Additive Models (Collarin, Fasiolo, Goude et al) Transformations of covariates are widely used in applied statistics to improve interpretability and to satisfy assumptions required for valid inference. More broadly, feature engi
Integrating Complex Covariate Transformations in Generalized Additive Models (Collarin, Fasiolo, Goude et al) Transformations of covariates are widely used in applied statistics to improve interpretability and to satisfy assumptions required for valid inference. More broadly, feature engi
link 📈🤖
ReLU-Based and DNN-Based Generalized Maximum Score Estimators (Chen, Gao, Wen) We propose a new formulation of the maximum score estimator that uses compositions of rectified linear unit (ReLU) functions, instead of indicator functions as in Manski (1975,1985), to encode the sign alignmen
ReLU-Based and DNN-Based Generalized Maximum Score Estimators (Chen, Gao, Wen) We propose a new formulation of the maximum score estimator that uses compositions of rectified linear unit (ReLU) functions, instead of indicator functions as in Manski (1975,1985), to encode the sign alignmen
November 25, 2025 at 5:13 PM
link 📈🤖
ReLU-Based and DNN-Based Generalized Maximum Score Estimators (Chen, Gao, Wen) We propose a new formulation of the maximum score estimator that uses compositions of rectified linear unit (ReLU) functions, instead of indicator functions as in Manski (1975,1985), to encode the sign alignmen
ReLU-Based and DNN-Based Generalized Maximum Score Estimators (Chen, Gao, Wen) We propose a new formulation of the maximum score estimator that uses compositions of rectified linear unit (ReLU) functions, instead of indicator functions as in Manski (1975,1985), to encode the sign alignmen